Networks and Big Data Short Course (video)

This accessible three-day executive education course will teach participants to meaningfully understand and interpret network analyses of economic, financial, marketing, and social systems.

About this NYC-based course:

This accessible three-day executive education course provides an intensive introduction to the field of complexity as it relates to networks and big data. Through lectures, exercises, and interactive discussions with prominent SFI faculty and your fellow participants, you will learn how methods and tools at the forefront of complexity science are being applied to understanding, modeling, and predicting the behavior of networked systems across many disciplines. Particular emphasis will be paid to understanding the implications of network analysis in the domains of economics, finance, marketing, and social networks. This course does not require any prior knowledge of network math or computer science. By the end of the course, participants will be able to meaningfully interpret and understand analyses based on network math.

About SFI:

The Santa Fe Institute (SFI) is the leader in complex systems theory research. SFI was established by an interdisciplinary group of intellectual leaders and renegades, including Nobel Laureates Murray Gell-Mann (Physics), Kenneth Arrow (Economics), and Phil Anderson (Physics). Today SFI is a nonprofit research institute with a notable reputation within academia. Some examples of techniques and theories pursued at SFI include, agent-based modeling, genetic algorithms, network theory, evolutionary game theory, non-linear dynamics, statistical physics, scaling theory, the theory of collective computation, information theory, and maximum entropy methods. ASU's transformative president Michael Crow has called SFI "a modern version of Plato's Academy." Rolling Stone Magazine called the SFI "a sort of Justice League of renegade geeks, where teams of scientists from disparate fields study the Big Questions." Pulitzer Prize winning novelist Cormac McCarthy comments on the Institute can be found here: goo.gl/viOH20

About SFI Short Courses:

Each year SFI offers between two and three short courses. The purpose of these short courses is to provide actionable insights from Institute's latest theoretical work to the public. Each year one short course is run at SFI in Santa Fe, New Mexico. A second short course is run in a different city each year. In 2016 the remote city was Austin, Texas. For 2017, the remote city is New York City. More information about SFI's efforts to facilitate actionable insights from its theoretical work can be found here: santafe.edu/ACtioN. More information about SFI's general education programs can be found here: santafe.edu/education.

Course overview:

The course is particularly focused on understanding the implications of network analysis in the domains of economics, finance, marketing, and social networks.

We are in an age of information, with nearly every scientific field awash in new data. Thus, making sense of large sets of real-world data stands as a preeminent challenge for modern science. A preponderance of these new massive new data sets are generated by networked adaptive systems, also know as complex systems. The networked nature of these datasets' underlying generative mechanisms underscores the potential value of statistically analyzing the network structure of the data. However, most decision-makers have not been trained to correctly interpret and understand the implications of such analyses. This course seeks to address this crucial gap.

Through lectures, exercises, and interactive discussions with prominent SFI faculty and your fellow participants, you will learn how methods and tools at the forefront of complexity science are being applied to modeling, predicting, and impacting the behavior of networked systems across many disciplines. This course is specifically designed for business and financial professionals. No background in science or mathematics is required.

Program Staff:

Michelle Girvan is an Associate Professor in the Department of Physics and the Institute for Physical Science and Technology at the University of Maryland, College Park. She is also a member of the External Faculty at the Santa Fe Institute. Her research operates at the intersection of statistical physics, nonlinear dynamics, and computer science and has applications to social, biological, and technological systems. More specifically, her work focuses on complex networks and often falls within the fields of computational biology and sociophysics. While some of the research is purely theoretical, Girvan has become increasingly involved in using empirical data to inform and validate mathematical models. She is perhaps best know for her contribution to the Girvan-Newman Algorithm for the identification of distinct communities within networked systems.